Network traffic forecasting for non-ticketed events

ABSTRACT

An example method includes estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings, estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event, calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic, and implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event.

This application is a continuation of U.S. patent application Ser. No.16/679,732, filed Nov. 11, 2019, now U.S. Pat. No. 11,172,381, which isa continuation of U.S. patent application Ser. No. 15/783,789, filedOct. 13, 2017, now U.S. Pat. No. 10,477,416, both of which are hereinincorporated by reference in their entirety.

The present disclosure relates generally to the operation ofcommunications networks, and relates more particularly to devices,non-transitory computer readable media, and methods for forecastingnetwork traffic for events.

BACKGROUND

Events that draw a large number of attendees, such as sporting events,concerts, public demonstrations, parades, protests, and the like, maytemporarily increase the traffic on a communications network in alocalized area. For instance, participants in a public demonstrationalong several blocks of city streets may upload messages, photos, and/orvideos to their social media accounts in real time during thedemonstration. Thus, the amount of network traffic originating andterminating along those blocks during the demonstration may increasesignificantly over what is typical for the location (i.e., during timesin which no events are occurring).

SUMMARY

An example method includes estimating a number of people expected toattend a non-ticketed event, based on electronic data including socialmedia postings, estimating a per-person amount of network trafficexpected to be generated on a communications network during thenon-ticketed event, based at least in part on historical per-personnetwork traffic statistics for a historical event of a similar type tothe non-ticketed event, calculating an amount of total network trafficexpected to be generated during the non-ticketed event, based at leaston the number of people expected to attend the non-ticketed event andthe per-person amount of network traffic, and implementing amodification to an infrastructure of the communications network in ageographic location of the non-ticketed event, based at least in part onthe amount of total network traffic expected to be generated during thenon-ticketed event.

An example system includes a processor and a computer readable mediumstoring instructions which, when executed by the processor, cause theprocessor to perform operations. The operations include estimating anumber of people expected to attend a non-ticketed event, based onelectronic data including social media postings, estimating a per-personamount of network traffic expected to be generated on a communicationsnetwork during the non-ticketed event, based at least in part onhistorical per-person network traffic statistics for a historical eventof a similar type to the non-ticketed event, calculating an amount oftotal network traffic expected to be generated during the non-ticketedevent, based at least on the number of people expected to attend thenon-ticketed event and the per-person amount of network traffic, andimplementing a modification to an infrastructure of the communicationsnetwork in a geographic location of the non-ticketed event, based atleast in part on the amount of total network traffic expected to begenerated during the non-ticketed event.

An example computer readable medium stores instructions which, whenexecuted by the processor, cause the processor to perform operations.The operations include estimating a number of people expected to attenda non-ticketed event, based on electronic data including social mediapostings, estimating a per-person amount of network traffic expected tobe generated on a communications network during the non-ticketed event,based at least in part on historical per-person network trafficstatistics for a historical event of a similar type to the non-ticketedevent, calculating an amount of total network traffic expected to begenerated during the non-ticketed event, based at least on the number ofpeople expected to attend the non-ticketed event and the per-personamount of network traffic, and implementing a modification to aninfrastructure of the communications network in a geographic location ofthe non-ticketed event, based at least in part on the amount of totalnetwork traffic expected to be generated during the non-ticketed event.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example network, or system in which examples ofthe present disclosure for forecasting network traffic may operate;

FIG. 2 illustrates a flowchart of an example method for forecastingnetwork traffic for events, in accordance with the present disclosure;

FIG. 3 illustrates a flowchart of an example method for estimating thenumber of people expected to attend an event, e.g., in accordance withthe method illustrated in FIG. 2;

FIG. 4 illustrates a flowchart of an example method for estimating theaverage network traffic per user endpoint device an event, e.g., inaccordance with the method illustrated in FIG. 2; and

FIG. 5 illustrates an example of a computing device, or computingsystem, specifically programmed to perform the steps, functions, blocks,and/or operations described herein.

To facilitate understanding, similar reference numerals have been used,where possible, to designate elements that are common to the figures.

DETAILED DESCRIPTION

The present disclosure broadly discloses methods, computer-readablemedia, and devices for forecasting network traffic for events. Asdiscussed above, events that draw a large number of attendees, such assporting events, concerts, public demonstrations, parades, protests, andthe like, may temporarily increase the traffic on a communicationsnetwork in a localized area. For instance, participants in a publicdemonstration along several blocks of city streets may upload messages,photos, and/or videos to their social media accounts in real time duringthe demonstration. Thus, the amount of network traffic originating andterminating along those blocks during the demonstration may increasesignificantly over what is typical for the location. If the localnetwork infrastructure is not equipped to handle this increase intraffic, users of the communications network may experience adegradation in the network performance.

Although it may be possible to increase the capacity of the network ifan increase in traffic is expected, it may be difficult to predict theamount by which the capacity needs to be increased, especially forevents for which attendance is open-ended. For instance, events that arenot ticketed could draw a theoretically limitless number of attendees,and this number could not be accurately ascertained in advance.Moreover, the number of attendees at such events may be subject togreater variation over time than ticketed events (e.g., attendees couldarrive well after the start of the event, or leave well before the end).Similarly, in the case of a sudden event such as a natural disaster,large-scale accident, or attack, it is a challenge to determine how manypeople may be present at the scene of the event.

Examples of the present disclosure mine electronic data, includingsocial media postings, to estimate the number of people expected to bein attendance at an event. The event may be a planned event (e.g., aplanned public demonstration) or an unplanned event (e.g., a naturaldisaster or attack). In some examples, the event is a non-ticketedevent. Based on historical information about similar events, theper-person (i.e., per-attendee) network traffic can be estimated togenerate an estimate of the total traffic demand on the communicationsnetwork in the area surrounding the event, during the event. Arecommendation may be generated based on the estimate of the totaltraffic demand, where the recommendation includes making modificationsto the network infrastructure at least in the area surrounding the event(e.g., temporarily deploying additional mobile evolved node B's(eNodeBs) or other hardware). The modifications may be designed tominimize the degradation in network performance that may be experiencedby network users in the area surrounding the event, during the event.

To better understand the present disclosure, FIG. 1 illustrates anexample network, or system 100 in which examples of the presentdisclosure for forecasting network traffic may operate. In one example,the system 100 includes a telecommunication service provider network170. The telecommunication service provider network 170 may comprise acellular network 101 (e.g., a 4G/Long Term Evolution (LTE) network, a4G/5G hybrid network, or the like), a service network 140, and a corenetwork, e.g., an IP Multimedia Subsystem (IMS) core network 115. Thesystem 100 may further include other networks 180 connected to thetelecommunication service provider network 105. FIG. 1 also illustratesvarious mobile endpoint devices 116 and 117, e.g., user equipment oruser endpoints (UE). The mobile endpoint devices UE 116 and 117 may eachcomprise a cellular telephone, a smartphone, a tablet computing device,a laptop computer, a pair of computing glasses, a wireless enabledwristwatch, or any other cellular-capable mobile telephony and computingdevices (broadly, “mobile endpoint devices”).

In one example, the cellular network 101 may comprise an access network103 and a core network, Evolved Packet Core (EPC) network 105. In oneexample, the access network 103 comprises a cloud RAN. For instance, acloud RAN is part of the 3^(rd) Generation Partnership Project (3GPP) 5Gspecifications for mobile networks. As part of the migration of cellularnetworks towards 5G, a cloud RAN may be coupled to an EPC network untilnew cellular core networks are deployed in accordance with 5Gspecifications. In one example, access network 103 may include cellsites 111 and 112 and a baseband unit (BBU) pool 114. In a cloud RAN,radio frequency (RF) components, referred to as remote radio heads(RRHs), may be deployed remotely from baseband units, e.g., atop cellsite masts, buildings, and so forth. In one example, the BBU pool 114may be located at distances as far as 20-80 kilometers or more away fromthe antennas/remote radio heads of cell sites 111 and 112 that areserviced by the BBU pool 114. It should also be noted in accordance withefforts to migrate to 5G networks, cell sites may be deployed with newantenna and radio infrastructures such as multiple input multiple output(MIMO) antennas, and millimeter wave antennas. In this regard, a cell,e.g., the footprint or coverage area of a cell site, may, in someinstances be smaller than the coverage provided by NodeBs or eNodeBs of3G-4G RAN infrastructure. For example, the coverage of a cell siteutilizing one or more millimeter wave antennas may be 1000 feet or less.

Although cloud RAN infrastructure may include distributed RRHs andcentralized baseband units, a heterogeneous network may include cellsites where RRH and BBU components remain co-located at the cell site.For instance, cell site 113 may include RRH and BBU components. Thus,cell site 113 may comprise a self-contained “base station.” With regardto cell sites 111 and 112, the “base stations” may comprise RRHs at cellsites 111 and 112 coupled with respective baseband units of BBU pool114.

In one example, the EPC network 105 provides various functions thatsupport wireless services in the LTE environment. In one example, EPCnetwork 105 is an Internet Protocol (IP) packet core network thatsupports both real-time and non-real-time service delivery across a LTEnetwork, e.g., as specified by the 3GPP standards. In one example, allcell sites in the access network 103 are in communication with the EPCnetwork 105 via baseband units in BBU pool 114. In operation, mobileendpoint device UE 116 may access wireless services via the cell site111 and mobile endpoint device UE 117 may access wireless services viathe cell site 112 located in the access network 103. It should be notedthat any number of cell sites can be deployed in access network. In oneillustrative example, the access network 103 may comprise one or morecell sites.

In EPC network 105, network devices such as Mobility Management Entity(MME) 107 and Serving Gateway (SGW) 108 support various functions aspart of the cellular network 101. For example, MME 107 is the controlnode for the LTE access network. In one embodiment, MME 107 isresponsible for UE (User Equipment) tracking and paging (e.g., such asretransmissions), bearer activation and deactivation process, selectionof the SGW, and authentication of a user. In one embodiment, SGW 108routes and forwards user data packets, while also acting as the mobilityanchor for the user plane during inter-cell handovers and as the anchorfor mobility between LTE and other wireless technologies, such as 2G and3G wireless networks.

In addition, EPC network 105 may comprise a Home Subscriber Server (HSS)109 that contains subscription-related information (e.g., subscriberprofiles), performs authentication and authorization of a wirelessservice user, and provides information about the subscriber's location.The EPC network 105 may also comprise a packet data network (PDN)gateway 110 which serves as a gateway that provides access between theEPC network 105 and various data networks, e.g., service network 140,IMS core network 115, other network(s) 180, and the like. The packetdata network gateway is also referred to as a PDN gateway, a PDN GW or aPGW. In addition, the EPC network 105 may include a Diameter routingagent (DRA) 106, which may be engaged in the proper routing of messagesbetween other elements within EPC network 105, and with other componentsof the system 100, such as a call session control function (CSCF) (notshown) in IMS core network 115. For clarity, the connections between DRA106 and other components of EPC network 105 are omitted from theillustration of FIG. 1.

In one example, service network 140 may comprise one or more devices,such as application server (AS) 145 for providing services tosubscribers, customers, and or users. For example, telecommunicationservice provider network 170 may provide a cloud storage service, webserver hosting, social media applications, and other services. As such,service network 140 may represent aspects of telecommunication serviceprovider network 170 where infrastructure for supporting such servicesmay be deployed. In one example, AS 145 may comprise all or a portion ofa computing device or system, such as computing system 500, and/orprocessing system 502 as described in connection with FIG. 5 below,specifically configured to provide one or more service functions inaccordance with the present disclosure. Although a single applicationserver, AS 145, is illustrated in service network 140, it should beunderstood that service network 140 may include any number of componentsto support one or more services that may be provided to one or moresubscribers, customers, or users by the telecommunication serviceprovider network 170.

In one example, other networks 180 may represent one or more enterprisenetworks, a circuit switched network (e.g., a public switched telephonenetwork (PSTN)), a cable network, a digital subscriber line (DSL)network, a metropolitan area network (MAN), an Internet service provider(ISP) network, and the like. In one example, the other networks 180 mayinclude different types of networks. In another example, the othernetworks 180 may be the same type of network. In one example, the othernetworks 180 may represent the Internet in general.

In accordance with the present disclosure, any one or more of thecomponents of EPC network 105 may comprise network functionvirtualization infrastructure (NFVI), e.g., SDN host devices (i.e.,physical devices) configured to operate as various virtual networkfunctions (VNFs), such as a virtual MME (vMME), a virtual HHS (vHSS), avirtual serving gateway (vSGW), a virtual packet data network gateway(vPGW), and so forth. For instance, MME 107 may comprise a vMME, SGW 108may comprise a vSGW, and so forth. In this regard, the EPC network 105may be expanded (or contracted) to include more or less components thanthe state of EPC network 105 that is illustrated in FIG. 1. In thisregard, the EPC network 105 may also include a self-optimizing network(SON)/software defined network (SDN) controller 190. In one example,SON/SDN controller 190 may function as a self-optimizing network (SON)orchestrator that is responsible for activating and deactivating,allocating and deallocating, and otherwise managing a variety of networkcomponents. For instance, SON/SDN controller 190 may activate anddeactivate antennas/remote radio heads of cell sites 111 and 112,respectively, may allocate and deactivate baseband units in BBU pool114, and may perform other operations for activating antennas based upona location and a movement of a group of mobile endpoint devices, inaccordance with the present disclosure.

In one example, SON/SDN controller 190 may further comprise a SDNcontroller that is responsible for instantiating, configuring, managing,and releasing VNFs. For example, in a SDN architecture, a SDN controllermay instantiate VNFs on shared hardware, e.g., NFVI/host devices/SDNnodes, which may be physically located in various places. In oneexample, the configuring, releasing, and reconfiguring of SDN nodes iscontrolled by the SDN controller, which may store configuration codes,e.g., computer/processor-executable programs, instructions, or the likefor various functions which can be loaded onto an SDN node. In anotherexample, the SDN controller may instruct, or request an SDN node toretrieve appropriate configuration codes from a network-basedrepository, e.g., a storage device, to relieve the SDN controller fromhaving to store and transfer configuration codes for various functionsto the SDN nodes.

In accordance with the present disclosure, SON/SDN controller 190 maytherefore control various components within EPC network 105 and/orwithin access network 103 to support the traffic that is accommodated bythe activation of antennas/remote radio heads of cell sites 111 and 112,respectively and the allocation of baseband units in BBU pool 114. Forinstance, SON/SDN controller 190 (e.g., performing functions of a SONorchestrator) may activate an antenna of cell site 111 and assign abaseband unit in BBU pool 114 when a group of mobile endpoint devices isdetected near the cell site 111. SON/SDN controller 190 (e.g.,performing functions of a SDN controller) may further instantiate VNFsto function as routers, switches, gateways, and the like to ensure thatsufficient backhaul resources are available for the traffic to transitthe access network 103 and/or EPC network 105. In addition, as mentionedabove, any one or more of the DRA 106, MME 107, SGW 108, HSS 109, andPGW 110 may comprise VNFs instantiated on host devices. As such, SON/SDNcontroller 190 may perform similar operations to instantiate, configure,reconfigure, and decommission such components in support of examples ofthe present disclosure for activating antennas based upon a location anda movement of a group of mobile endpoint devices.

In one example, SON/SDN controller 190 may comprise all or a portion ofa computing device or system, such as computing system 500, and/orprocessing system 502 as described in connection with FIG. 5 below, andmay be configured to provide one or more functions to support examplesof the present disclosure for forecasting network traffic for events,and for performing various other operations in accordance with thepresent disclosure. For example, SON/SDN controller 190 may work inconjunction with a cell site 111-113 and/or baseband unit of BBU pool114 to track the signaling patterns of sample user endpoint devices 116and/or 117, in connection with operations of the methods of FIGS. 2-4.For instance, SON/SDN controller 190 may store the observed signalingpatterns of sample user endpoint devices during an event, and thesesignalizing patterns may be referred to when estimating the networktraffic during future events of a similar type.

Accordingly, the SON/SDN controller 190 may be connected directly orindirectly to any one or more network elements of EPC network 105, andof the system 100 in general. Due to the relatively large number ofconnections available between SON/SDN controller 190 and other networkelements, none of the actual links to the application server are shownin FIG. 1. Similarly, intermediate devices and links between DRA 106,MME 107, SGW 108, eNodeBs 111 and 112, PDN gateway 110, and othercomponents of system 100 are also omitted for clarity, such asadditional routers, switches, gateways, and the like.

As further illustrated in FIG. 1, EPC network 105 may further include anapplication server (AS) 130, which may comprise all or a portion of acomputing device or system, such as computing system 500, and/orprocessing system 502 as described in connection with FIG. 5 below, andmay be configured to perform various operations in connection withforecasting network traffic for events, and for performing various otheroperations in accordance with the present disclosure. For instance, AS130 may host a web crawler or similar software application that minesthe Internet, including social media applications, for data relating tofuture and/or in-progress events. In this regard, AS 130 may maintaincommunications with BBU pool 114, cell sites 111-113, and so forth, viaPDN gateway 110 and SGW 108, for example. AS 130 may also have access tohistorical data regarding past events and associated network traffic,and may perform other operations based upon the historical data. Forinstance, AS 130 may generate an estimate of the network traffic that isexpected to be generated in a geographic area during an event, and mayfurther generate a recommendation for modifying the networkinfrastructure at least in the geographic area of the event toaccommodate the expected network traffic.

The foregoing description of the system 100 is provided as anillustrative example only. In other words, the example of system 100 ismerely illustrative of one network configuration that is suitable forimplementing embodiments of the present disclosure. As such, otherlogical and/or physical arrangements for the system 100 may beimplemented in accordance with the present disclosure. For example, thesystem 100 may be expanded to include additional networks, such asnetwork operations center (NOC) networks, additional access networks,and so forth. The system 100 may also be expanded to include additionalnetwork elements such as border elements, routers, switches, policyservers, security devices, gateways, a content distribution network(CDN) and the like, without altering the scope of the presentdisclosure. In addition, system 100 may be altered to omit variouselements, substitute elements for devices that perform the same orsimilar functions, combine elements that are illustrated as separatedevices, and/or implement network elements as functions that are spreadacross several devices that operate collectively as the respectivenetwork elements. For instance, in one example, SON/SDN controller 190may be spilt into separate components to operate as a SON orchestratorand a SDN controller, respectively. Similarly, although the SON/SDNcontroller 190 is illustrated as a component of EPC network 105, inanother example SON/SDN controller 190, and/or other network componentsmay be deployed in an IMS core network 115 instead of being deployedwithin the EPC network 105, or in other portions of system 100 that arenot shown, while providing essentially the same functionality.Similarly, functions described herein with respect to AS 130 mayalternatively or additional be provided by AS 145.

In addition, it should be noted that as used herein, the terms“configure,” and “reconfigure” may refer to programming or loading aprocessing system with computer-readable/computer-executableinstructions, code, and/or programs, e.g., in a distributed ornon-distributed memory, which when executed by a processor, orprocessors, of the processing system within a same device or withindistributed devices, may cause the processing system to perform variousfunctions. Such terms may also encompass providing variables, datavalues, tables, objects, or other data structures or the like which maycause a processing system executing computer-readable instructions,code, and/or programs to function differently depending upon the valuesof the variables or other data structures that are provided. As referredto herein a “processing system” may comprise a computing deviceincluding one or more processors, or cores (e.g., as illustrated in FIG.5 and discussed below) or multiple computing devices collectivelyconfigured to perform various steps, functions, and/or operations inaccordance with the present disclosure.

In addition, although aspects of the present disclosure have beendiscussed above in the context of a long term evolution (LTE)-basedwireless network, examples of the present disclosure are not so limited.Thus, the teachings of the present disclosure can be applied to othertypes of wireless networks (e.g., a 2G network, a 3G network, a 5Gnetwork, an integrated network, e.g., including any two or more of 2G-5Ginfrastructure and technologies, and the like), that are suitable foruse in connection with examples of the present disclosure forforecasting network traffic for events. For example, as illustrated inFIG. 1, the cellular network 101 may represent a “non-stand alone” (NSA)mode architecture where 5G radio access network components, such as a“new radio” (NR), “gNodeB” (or “gNB”), and so forth are supported by a4G/LTE core network (e.g., a Evolved Packet Core (EPC) network 105).However, in another example, system 100 may instead comprise a 5G“standalone” (SA) mode point-to-point or service-based architecturewhere components and functions of EPC network 105 are replaced by a 5Gcore network, which may include an access and mobility managementfunction (AMF), a user plane function (UPF), a session managementfunction (SMF), a policy control function (PCF), a unified datamanagement function (UDM), an authentication server function (AUSF), anapplication function (AF), a network repository function (NRF), and soon. For instance, in such a network, application server (AS) 130 of FIG.1 may represent an application function (AF) for forecasting networktraffic for events in accordance with various examples of the presentdisclosure. In addition, any one or more of cell sites 111-113 maycomprise 2G, 3G, 4G and/or LTE radios, e.g., in addition to 5G new radio(NR) functionality. For instance, in non-standalone (NSA) modearchitecture, LTE radio equipment may continue to be used for cellsignaling and management communications, while user data may rely upon a5G new radio (NR), including millimeter wave communications, forexample. Thus, these and other modifications are all contemplated withinthe scope of the present disclosure.

FIG. 2 illustrates a flowchart of an example method 200 for forecastingnetwork traffic for events, in accordance with the present disclosure.In one example, steps, functions and/or operations of the method 200 maybe performed by a device as illustrated in FIG. 1, e.g., an applicationserver, or any one or more components thereof. In one example, thesteps, functions, or operations of the method 200 may be performed by acomputing device or system 500, and/or a processing system 502 asdescribed in connection with FIG. 5 below. For instance, the computingdevice 500 may represent at least a portion of an application server inaccordance with the present disclosure. For illustrative purposes, themethod 200 is described in greater detail below in connection with anexample performed by an application server, such as 130 or 145 of FIG.1.

The method 200 begins in step 202. In step 204, the application serveridentifies an event. In one example, identification of the eventincludes identifying the name, date, time, location/venue, and/ormaximum capacity of the event. The event may be a planned event that isscheduled to take place at some time in the future (e.g., a plannedpublic demonstration), a planned event that is currently ongoing, or anunplanned, suddenly occurring event (e.g., a natural disaster). In oneexample, the event is a non-ticketed event, i.e., tickets or other formsof passes are not issued to control or limit attendance numbers. In oneexample, the event is explicitly identified by a user. For instance, auser may identify a specific event as a means of initiating a forecastof the network traffic that the event is expected to generate. Inanother example, the event may be identified without user input, e.g.,by mining news items, social media, venue web sites, event planning websites (e.g., websites promoting various events), ticketing websites andother sources of electronic data using a web crawler or similartechnology. In this case, natural language understanding techniques maybe used to identify relevant information.

In step 206, the application server generates an estimate of theexpected attendance at the event. For instance, the application servermay estimate how many people are expected to attend the event. In oneexample, the application server generates this estimate by mining newsitems, social media, venue web sites, and other sources of data using aweb crawler or similar technology. For instance, users of a social mediaapplication may post updates expressing their interest in the event(e.g., by expressing an explicit intent to attend the event or bybookmarking, re-posting, or “liking” postings related to the event).Each individual social media user who posts or bookmarks such an updatemay be considered as a potential attendee of the event. Information likethis can be mined from various sources and analyzed (e.g., using naturallanguage processing) to determine an approximate number of people whointend to attend the event. Such information may be valuable for largescale events, and particularly for events that do not require a ticketfor attendance. In the case of ticketed events, the application servermay also have access to information about the number of tickets offeredfor sale or that have been sold. For instance, information may beobtained from online portals through which tickets can be purchased, orfrom web sites related to event venues (which may provide informationregarding seating capacity for various types of events). One example ofa specific method for estimating the number of people expected to attendan event is described in greater detail in connection with FIG. 3.

In step 208, the application server estimates the per-user (or per-userendpoint device) network traffic for the event. In one example, theper-user network traffic is estimated based at least in part on the typeor class of the event. For instance, a historical event of a similartype can be identified, and the per-user traffic for the historicalevent can be used to extrapolate the expected per-user traffic duringthe identified event. Available per-user traffic statistics for thehistorical event might include, for example, key performance indicatorslike average upload data rate, average download data rate,accessibility, retainability every x minutes, and other data that can beextracted from call trace records (e.g., using the historical event timeand user locations to filter relevant traffic). Using the per-usertraffic of the historical event, the per-user traffic for the event canbe predicted in one example by performing a time series estimate. Sincetraffic demand tends to increase steadily with time, the time series mayadjust the estimate to account for future increases in demand. Oneexample of a specific method for estimating the per-user network trafficfor the event is described in greater detail in connection with FIG. 4.

In step 210, the application server predicts the total network trafficdemand for the event, based in part on the estimate of the expectedattendance and on the estimated per-user traffic. In one example, thetotal traffic demand is predicted by multiplying the estimate of theexpected attendance by the estimated per-user traffic.

In step 212, the application server predicts the total network trafficdemand at a geographical location of the event for a defined window oftime (e.g., x minutes). The geographical location of the event mayinclude the event venue (e.g., a stadium), plus some defined radius ofdistance (e.g., x miles) around the event venue. Thus, the total trafficdemand may include not just the estimated network traffic generated byattendees of the event, but also the estimated network traffic generatedby others in the vicinity of the event, who may not be attending theevent (e.g., workers in office buildings along a parade route, ortailgating by individuals in the parking lot of a large stadium for asports event who do not have tickets to the sports event itself).

In step 214, the application server generates a recommendation, based atleast in part on the predicted total traffic demand for the event (aspredicted in step 210) and on the predicted total traffic demand for thegeographic location of the event (as predicted in step 212). Therecommendation may comprise a temporary modification to the networkinfrastructure at least in the geographic area of the event, toaccommodate the expected network traffic. For instance, therecommendation may involve the deployment of one or more mobile eNodeBsin the geographic area of the event. In further examples, therecommendation may involve the strategic placement of additionalantennas to form a distributed antenna system (DAS) that maximizes radiocoverage in the geographic location of the event. In further examplesstill, the recommendation may involve the deployment of portable basestations, such as cells on wheels (COWs). COWs temporarily increasecommunication capacity and provide free WiFi/LTE-U (e.g., small cellaccess points for offloading Internet traffic from cellular to cellularbase stations.

In a further embodiment still, as 5G networks are being deployed andtraffic estimation is considered in view of network virtualization,virtualized network functions (VNFs) such as virtual mobility managemententities (vMMEs), virtual evolved packet data gateways (vePDGs), virtualpacket data network gateways (vPDGs), and virtual baseband units (vBBUs)can be recommended for dynamic instantiation to improve networkcapacity.

In any of these cases, generating a recommendation may further involveautomatically and dynamically implementing any of the recommendations.In one example, implementing a recommendation may involve sendingsignals to mobile equipment (e.g., mobile eNodeB's, antennas of a DAS,COWs, etc.) that direct the equipment to particular locations and/oractivate the equipment and its connections to the communicationsnetwork. In another example, implementing a recommendation may involveautomatic provisioning of appropriate VNFs by a SON/SDN controller andthe like. The method 200 ends in step 216.

FIG. 3 illustrates a flowchart of an example method 300 for estimatingthe number of people expected to attend an event, e.g., in accordancewith step 206 of the method 200 illustrated in FIG. 2. As such, in oneexample, steps, functions and/or operations of the method 300 may beperformed by a device as illustrated in FIG. 1, e.g., an applicationserver, or any one or more components thereof. In one example, thesteps, functions, or operations of the method 300 may be performed by acomputing device or system 500, and/or a processing system 502 asdescribed in connection with FIG. 5 below. For instance, the computingdevice 500 may represent at least a portion of an application server inaccordance with the present disclosure. For illustrative purposes, themethod 300 is described in greater detail below in connection with anexample performed by an application server, such as 130 or 145 of FIG.1.

The method 300 begins in step 302. In step 304, the application serverclassifies the event by performing linear discriminant analysis (LDA) oninformation about the event that has been collected from electronic datasources such as social media (e.g., according to the method 200 asdescribed above). As discussed above, the event classification maycomprise a large-scale sporting event (e.g., a professional sportschampionship game), a small-scale sporting event (e.g., a college sportsgame), a large-scale concert (e.g., a music festival), a small-scaleconcert (e.g., a local music event), a family event (e.g., a parade orfair), a public demonstration (e.g., a march), a protest, a naturaldisaster (e.g., a flash flood or tornado), a large-scale accident (e.g.,a water main break or train accident), an attack (e.g., a bombing orshooting), or the like. The event may be further classified as a plannedor unplanned event.

In step 306, the application server scores the popularity of the event,again based at least in part on the information about the event that hasbeen collected from electronic data sources, such as social media (e.g.,according to the method 200 as described above). In one example, theapplication server performs sentiment analysis on the informationcollected from social media. This analysis may consider the type of theevent (e.g., as determined in step 304), the performer(s) involved inthe event (if any), historical attendance at events involving theperformer(s), and the number of “likes” or similar endorsements receivedby the performer(s) by social media users.

In one particular example, the popularity of the event and theperformer(s) involved are scored individually. For instance, thepopularity P_(E) of an event may be calculated as:

P _(E)=(C*comments+L*likes)*100/views  (EQN. 1)

where C and L are constants, the term “comments” is the number of usercomments posted on a social media page associated with the event, theterm “likes” is the number of likes or similar user endorsements (i.e.,excluding comments) posted on the social media page associated with theevent, and the term “views” is the number of views (e.g., total views orviews from individual users) accumulated by the social media pageassociated with the event.

Similarly, the score P_(P) of a performer may be calculated as:

P _(P)=(C*comments+L*likes)*100/views  (EQN. 2).

where C and L are constants, the term “comments” is the number of usercomments posted on a social media page associated with the performer,the term “likes” is the number of likes or similar user endorsements(i.e., excluding comments) posted on the social media page associatedwith the performer, and the term “views” is the number of views (e.g.,total views or views from individual users) accumulated by the socialmedia page associated with the performer.

The popularity of the event may also be computed based on informationthat is not necessarily collected from a dedicated social media page forthe event. For instance, the event may be mentioned in user feeds on amicroblogging site. In this case, the popularity P_(E) of the event maybe calculated as:

$\begin{matrix}{P_{E} = \frac{\#\mspace{14mu}{of}\mspace{14mu}{mentions}\mspace{14mu}{of}\mspace{14mu}{event}\mspace{14mu}{in}\mspace{14mu}{last}\mspace{14mu} x\mspace{14mu}{minutes}}{\begin{matrix}{\#\mspace{14mu}{of}\mspace{14mu}{mentions}\mspace{14mu}{of}\mspace{14mu}{event}\mspace{14mu}{originating}\mspace{14mu}{at}} \\{{geographical}\mspace{14mu}{location}\mspace{14mu}{of}\mspace{14mu}{event}}\end{matrix}}} & \left( {{EQN}.\mspace{14mu} 3} \right)\end{matrix}$

where the geographical location of the event may encompass a particulargeographical radius (e.g., within y miles of event center) or a morespecific location (e.g., a particular park or sports arena).

In another example, the popularity of the event may be approximated asthe Klout Score for the performer(s) involved in the event, as obtainedfrom Lithium Technologies' KLOUT social media data analyticsapplication. In this example, the Klout Score of the performer(s) iscomputed from a plurality of major social networking sites, consideringcomplex social interactions in social media. In one example, the KloutScore for the performer(s) is considered in relation to at least oneother performer. In a further example, the Klout Score may be scaled upwith respect to the highest scoring performer who previously performedat the venue or location at which the event is to take place.

In step 308, the application server estimates the number of peopleexpected to attend the event. In one example, the number N of peopleexpected to attend the event is estimated as:

N=popularity score*maximum capacity of event  (EQN. 4)

where the popularity score may be computed according to any of EQNs. 1-3(or estimated according to the Klout Score) as a percentage. Thus, thenumber of people expected to attend the event is a fraction orpercentage of the event's maximum capacity. In some examples, e.g.,where the event is not ticketed or the event venue's capacity isopen-ended, the maximum capacity of the event may be estimated. Forinstance, the maximum capacity may be estimated as the number ofattendees of the most well-attended past event at the venue or as thenumber of attendees who attended a similar event at a different venue.In a further example, the estimated number of people expected to attendthe event can be refined based on real-time estimates of the road orfoot traffic in the vicinity of the event, during a window of time thatencompasses the duration of the event. In this case, “real-time”indicates the data is delivered to the application server substantiallyimmediately after it is logged, subject to any network delays. The“vicinity” of the event may be some defined geographic area thatencompasses the event venue, plus a defined radius of distance (e.g., xmiles) around the event venue. The estimates of the road and foottraffic may be obtained from road traffic sensors placed within thisvicinity. In one example, estimates from these sensors may be used toestimate the number of people present at an unplanned event, such as anatural disaster or other sudden events. The method 300 ends in step310.

FIG. 4 illustrates a flowchart of an example method 400 for estimatingthe average network traffic per user endpoint device at an event, e.g.,in accordance with step 208 of the method 200 illustrated in FIG. 2. Assuch, in one example, steps, functions and/or operations of the method400 may be performed by a device as illustrated in FIG. 1, e.g., anapplication server, or any one or more components thereof. In oneexample, the steps, functions, or operations of the method 400 may beperformed by a computing device or system 500, and/or a processingsystem 502 as described in connection with FIG. 5 below. For instance,the computing device 500 may represent at least a portion of anapplication server in accordance with the present disclosure. Forillustrative purposes, the method 400 is described in greater detailbelow in connection with an example performed by an application server,such as 130 or 145 of FIG. 1.

The method 400 begins in step 402. In step 404, the application serveridentifies an historical event or events that are similar to the event.For instance, if the event is a professional sports championship game,similar historical events would include other professional sportschampionship games (possibly of the same league) rather than, forexample, concerts that occurred at the same venue.

In step 406, the per-user endpoint device network traffic of the eventis estimated based at least in part on the per-user endpoint devicenetwork traffic observed at the similar historical event(s). In oneexample, the per-user endpoint device network traffic of the event isestimated using a regression analysis of the similar historical event(s)as:

N(t)=(T(t)+S(t)+R(t))  (EQN. 5)

where N(t) represents the network traffic value as a function of time,S(t) is a seasonality component (e.g., representing the day of theweek), T(t) is a trend component as a function of time (e.g., to accountfor network traffic increases as demand for video and other media fromthe event increases), and R(t) is an irregular component representing ameasure of error in the estimation.

In the case of EQN. 5, the trend component T(t) assumes that the growthin network traffic is exponential. In this regard, the trend componentT(t) may be modeled as:

(T(t+1))=b0+b11+T(t)  (EQN. 6)

where b0 and b1 are constants. More specifically b0 is a constantrepresenting a bias and may be calculated as an average network trafficload over a set of training examples. b1 is a constant representing acoefficient of T(t) which captures a dependency on a previous time.These constants are estimated based on prediction models and data athand.

The seasonality component S(t) may be modeled to consider the trafficvariation over weekdays. For instance, the traffic can be modeled as acyclic component according to:

S(t+1)=a|cos(wt+o)|+S(t)  (EQN. 7)

where a is a constant that represents a highest magnitude of change dueto seasonality, w represents the frequency of the change due toseasonality, and o represents an offset at time t=0. As a whole, EQN. 7models periodicity of the changes in network traffic over time.

In another example, rather than using a time series to estimate theper-user endpoint device network traffic, a deep recurrent neuralnetwork may be used. In this case, the neural network hidden layer maymodel the seasonality, burst, and trend components without explicitparameterization. However, more data will be needed to train thenetwork. The method 400 ends in step 408.

In addition, although not specifically specified, one or more steps,functions or operations of the respective methods 200-400 may include astoring, displaying and/or outputting step as required for a particularapplication. In other words, any data, records, fields, and/orintermediate results discussed in the method can be stored, displayedand/or outputted either on the device executing the method or to anotherdevice, as required for a particular application. Furthermore, steps,blocks, functions or operations in any of FIGS. 2-4 that recite adetermining operation or involve a decision do not necessarily requirethat both branches of the determining operation be practiced. In otherwords, one of the branches of the determining operation can be deemed asan optional step. Furthermore, steps, blocks, functions or operations ofthe above described method(s) can be combined, separated, and/orperformed in a different order from that described above, withoutdeparting from the example examples of the present disclosure.

FIG. 5 depicts a high-level block diagram of a computing device orprocessing system specifically programmed to perform the functionsdescribed herein. As depicted in FIG. 5, the processing system 500comprises one or more hardware processor elements 502 (e.g., a centralprocessing unit (CPU), a microprocessor, or a multi-core processor), amemory 504 (e.g., random access memory (RAM) and/or read only memory(ROM)), a module 505 for forecasting network traffic for events, andvarious input/output devices 506 (e.g., storage devices, including butnot limited to, a tape drive, a floppy drive, a hard disk drive or acompact disk drive, a receiver, a transmitter, a speaker, a display, aspeech synthesizer, an output port, an input port and a user inputdevice (such as a keyboard, a keypad, a mouse, a microphone and thelike)). Although only one processor element is shown, it should be notedthat the computing device may employ a plurality of processor elements.Furthermore, although only one computing device is shown in the figure,if any one or more of the methods 200-400 as discussed above areimplemented in a distributed or parallel manner for a particularillustrative example, i.e., the steps of the above methods 200-400,respectively, or each of the entire methods 200-400, respectively, isimplemented across multiple or parallel computing devices, e.g., aprocessing system, then the computing device of this figure is intendedto represent each of those multiple computing devices.

Furthermore, one or more hardware processors can be utilized insupporting a virtualized or shared computing environment. Thevirtualized computing environment may support one or more virtualmachines representing computers, servers, or other computing devices. Insuch virtualized virtual machines, hardware components such as hardwareprocessors and computer-readable storage devices may be virtualized orlogically represented. The hardware processor 502 can also be configuredor programmed to cause other devices to perform one or more operationsas discussed above. In other words, the hardware processor 502 may servethe function of a central controller directing other devices to performthe one or more operations as discussed above.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable gatearray (PGA) including a Field PGA, or a state machine deployed on ahardware device, a computing device or any other hardware equivalents,e.g., computer readable instructions pertaining to the method discussedabove can be used to configure a hardware processor to perform thesteps, functions and/or operations of the above disclosed methods200-400. In one example, instructions and data for the present module orprocess 505 for forecasting network traffic for events (e.g., a softwareprogram comprising computer-executable instructions) can be loaded intomemory 504 and executed by hardware processor element 502 to implementthe steps, functions or operations as discussed above in connection withthe illustrative methods 200-400. Furthermore, when a hardware processorexecutes instructions to perform “operations,” this could include thehardware processor performing the operations directly and/orfacilitating, directing, or cooperating with another hardware device orcomponent (e.g., a co-processor and the like) to perform the operations.

The processor executing the computer readable or software instructionsrelating to the above described method can be perceived as a programmedprocessor or a specialized processor. As such, the present module 505for forecasting network traffic for events (including associated datastructures) of the present disclosure can be stored on a tangible orphysical (broadly non-transitory) computer-readable storage device ormedium, e.g., volatile memory, non-volatile memory, ROM memory, RAMmemory, magnetic or optical drive, device or diskette and the like.Furthermore, a “tangible” computer-readable storage device or mediumcomprises a physical device, a hardware device, or a device that isdiscernible by the touch. More specifically, the computer-readablestorage device may comprise any physical devices that provide theability to store information such as data and/or instructions to beaccessed by a processor or a computing device such as a computer or anapplication server.

While various examples have been described above, it should beunderstood that they have been presented by way of illustration only,and not a limitation. Thus, the breadth and scope of any aspect of thepresent disclosure should not be limited by any of the above-describedexamples, but should be defined only in accordance with the followingclaims and their equivalents.

What is claimed is:
 1. A method comprising: estimating a number ofpeople expected to attend a non-ticketed event, based on electronic dataincluding social media postings; estimating a per-person amount ofnetwork traffic expected to be generated on a communications networkduring the non-ticketed event, based at least in part on historicalper-person network traffic statistics for a historical event of asimilar type to the non-ticketed event; calculating an amount of totalnetwork traffic expected to be generated during the non-ticketed event,based at least on the number of people expected to attend thenon-ticketed event and the per-person amount of network traffic; andimplementing a modification to an infrastructure of the communicationsnetwork in a geographic location of the non-ticketed event, based atleast in part on the amount of total network traffic expected to begenerated during the non-ticketed event.
 2. The method of claim 1,wherein the non-ticketed event is a planned event that is set to occurat a future time.
 3. The method of claim 1, wherein the non-ticketedevent is a planned event that is currently ongoing.
 4. The method ofclaim 1, wherein the non-ticketed event is an unplanned event that iscurrently ongoing.
 5. The method of claim 1, wherein the estimatingcomprises: generating a popularity score for the non-ticketed event,based at least in part on the social media postings; and calculating thenumber of people expected to attend the non-ticketed event as thepopularity score multiplied by a maximum capacity of the non-ticketedevent.
 6. The method of claim 5, wherein the popularity score iscalculated as (C*comments+L*likes)*100/views, wherein C and L areconstants, comments is a number of user comments posted on a socialmedia page associated with a performer associated with the non-ticketedevent, likes is a number of user endorsements posted on the social mediapage associated with the performer, and views is a number of viewsaccumulated by the social media page associated with the performer. 7.The method of claim 1, wherein the number of people expected to attendthe non-ticketed event is updated based on real-time estimates of roadtraffic within a defined geographic area surrounding the non-ticketedevent.
 8. The method of claim 1, wherein the estimating the per-personamount of network traffic comprises performing a time series estimateusing the historical per-person network traffic statistics for thehistorical event.
 9. The method of claim 1, wherein the per-personamount of network traffic is calculated as a function of time as(T(t)+S(t)+R(t)), where S(t) is a seasonality component, T(t) is a trendcomponent as a function of time, and R(t) represents a measure of error,wherein the trend component assumes an exponential growth in theper-person amount of network traffic, and wherein the seasonalitycomponent is modeled to consider traffic variation over weekdays. 10.The method of claim 1, wherein the estimating the per-person amount ofnetwork traffic comprises using a deep recurrent neural network to modelseasonality, burst, and trend components of the historical per-personnetwork traffic statistics for the historical event.
 11. The method ofclaim 1, wherein the modification includes deploying temporaryinfrastructure and equipment to accommodate the amount of total networktraffic expected to be generated during the non-ticketed event.
 12. Themethod of claim 11, wherein the temporary infrastructure and equipmentincludes a mobile evolved nodeB.
 13. The method of claim 11, wherein thetemporary infrastructure and equipment includes a distributed antennasystem.
 14. The method of claim 11, wherein the temporary infrastructureand equipment includes a portable base station.
 15. The method of claim1, wherein the modification includes instantiating a virtual networkfunction.
 16. The method of claim 1, wherein the historical event isidentified by performing a linear discriminant analysis on theelectronic data in order to classify the non-ticketed event.
 17. Asystem comprising: a processor; and a computer-readable medium storinginstructions which, when executed by the processor, cause the processorto perform operations, the operations comprising: estimating a number ofpeople expected to attend a non-ticketed event, based on electronic dataincluding social media postings; estimating a per-person amount ofnetwork traffic expected to be generated on a communications networkduring the non-ticketed event, based at least in part on historicalper-person network traffic statistics for a historical event of asimilar type to the non-ticketed event; calculating an amount of totalnetwork traffic expected to be generated during the non-ticketed event,based at least on the number of people expected to attend thenon-ticketed event and the per-person amount of network traffic; andimplementing a modification to an infrastructure of the communicationsnetwork in a geographic location of the non-ticketed event, based atleast in part on the amount of total network traffic expected to begenerated during the non-ticketed event.
 18. A computer-readable mediumstoring instructions which, when executed by a processor, cause theprocessor to perform operations, the operations comprising: estimating anumber of people expected to attend a non-ticketed event, based onelectronic data including social media postings; estimating a per-personamount of network traffic expected to be generated on a communicationsnetwork during the non-ticketed event, based at least in part onhistorical per-person network traffic statistics for a historical eventof a similar type to the non-ticketed event; calculating an amount oftotal network traffic expected to be generated during the non-ticketedevent, based at least on the number of people expected to attend thenon-ticketed event and the per-person amount of network traffic; andimplementing a modification to an infrastructure of the communicationsnetwork in a geographic location of the non-ticketed event, based atleast in part on the amount of total network traffic expected to begenerated during the non-ticketed event.
 19. The computer-readablemedium of claim 18, wherein the estimating comprises: generating apopularity score for the non-ticketed event, based at least in part onthe social media postings; and calculating the number of people expectedto attend the non-ticketed event as the popularity score multiplied by amaximum capacity of the non-ticketed event.
 20. The computer-readablemedium of claim 19, wherein the popularity score is calculated as(C*comments+L*likes)*100/views, wherein C and L are constants, commentsis a number of user comments posted on a social media page associatedwith a performer associated with the non-ticketed event, likes is anumber of user endorsements posted on the social media page associatedwith the performer, and views is a number of views accumulated by thesocial media page associated with the performer.